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Results for “cell biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,423 records · Page 79Linked to original sources

bioRxiv2023

Massively Concurrent Sub-Cellular Traction Force Videography enabled by Single-Pixel Optical Tracers (SPOTs)

We report a large field-of-view and high-speed videography platform for measuring the sub-cellular traction forces of more than 10,000 biological cells over 13mm2 at 83 frames per second. Our Single-Pixel Optical Tracers (SPOT) tool uses 2-dimensional diffraction gratings embedded into a soft substrate to convert cells mechanical traction stress into optical colors detectable by a video camera. The platform measures the sub-cellular traction forces of diverse cell types, including tightly connected tissue sheets and near isolated cells. We used this platform to explore the mechanical wave propagation in a tightly connected sheet of Neonatal Rat Ventricular Myocytes (NRVMs) and discovered that the activation time of some tissue regions are heterogeneous from the overall spiral wave behavior of the cardiac wave. One-Sentence SummaryAn optical platform for fast, concurrent measurements of cell mechanics at 83 frames per second, over a large area of 13mm2.

bioengineering↗

bioRxiv2023

Vascularized tumor on a microfluidic chip to study mechanisms promoting tumor neovascularization and vascular targeted therapies

The cascade of events leading to tumor formation includes induction of a tumor supporting neovasculature as a primary hallmark of cancer. Developing vasculature is difficult to evaluate in vivo but can be captured using microfluidic chip technology and patient derived cells. Herein, we established an on chip approach to investigate the mechanisms promoting tumor vascularization and vascular targeted therapies via co-culture of metastatic renal cell carcinoma spheroids and endothelial cells in a 3D environment. Our model permitted real-time, high-resolution observation and assessment of tumor-induced angiogenesis, where endothelial cells sprout towards the tumor and mimic a vascular network. Bevacizumab, an angiogenic inhibitor, disrupted interactions between vessels and tumors, destroying the vascular network. The on chip approach enabled assessment of endothelial cell biology, vessels functionality, drug delivery, and molecular expression of PSMA. Finally, observations in the vascularized tumor on chip permitted direct and conclusive quantification of this therapy in weeks as opposed to months in a comparable animal model. TeaserVascularized tumor on microfluidic chip provides opportunity to study targeted therapies and improves preclinical drug discovery.

cancer biology↗

bioRxiv2023

Machine learning of cellular metabolic rewiring

Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. The model learned captures shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting potential organ-tailored cellular adaptations. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations. SignificanceMetabolic rewiring--the cellular adaptation to shifts in environment and nutrients--plays key roles in many contexts, including cancer metastasis. Traditional metabolomics often falls short of capturing the nuances of these metabolic shifts. This work introduces MetaboLiteLearner, a machine learning approach that harnesses the rich fragmentation patterns from electron ionization collected in scan mode during gas chromatography/mass spectrometry, paving the way for new insights into metabolic adaptations. Demonstrating its robustness on a breast cancer model, we highlight MetaboLiteLearners potential to reshape our understanding of metabolic rewiring, with implications in diagnostics, therapeutics, and basic cell biology.

bioinformatics↗

bioRxiv2023

Genome replication in asynchronously growing microbial populations

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. Qualitatively, in a growing cell culture, one expects that genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory to explain these data is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in Escherichia coli and in budding yeast. In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.

biophysics↗

bioRxiv2023

Onsager's variational principle in proliferating biological tissues, in presenceof activity and anisotropy

A hallmark of biological cells is their ability to proliferate and of tissues their ability to grow. This is common in morphogenesis and embryogenesis but also in pathological conditions such as tumour growth. To consider these tissues from a physical point of view, it is necessary to derive fundamental relationships, in particular for velocities and density components, taking into account growth terms, chemical factors and the symmetry of cells and tissues. The aim is then to develop a consistent coarse-grained approach to these complex systems, which exhibit proliferation, disorder, anisotropy and activity at small scales. To this end, Onsagers variational principle allows the systematic derivation of flux-force relations in systems out of equilibrium and the principle of the extremum of dissipation, first formulated by Rayleigh and revisited by Onsager, finally leads to a consistent formulation for a continuous approach in terms of a coupled set of partial differential equations. Considering the growth and death rates as fluxes, as well as the chemical reactions driving the cellular activities, we derive the momentum equations based on a leading order physical expansion. Furthermore, we illustrate the different interactions for systems with nematic or polar order at small scales, and numerically solve the resulting system of partial differential equations in relevant biophysical growth examples. To conclude, we show that Onsagers variational principle is useful for systematically exploring the different scenarios in proliferating systems, and how morphogenesis depends on these interactions.

biophysics↗

bioRxiv2023

Telomere-to-telomere Schizosaccharomyces japonicus genome assembly reveals hitherto unknown genome features

Schizosaccharomyces japonicus belongs to the single-genus class Schizosaccharomycetes, otherwise known as fission yeasts. As part of a composite model system with its widely studied S. pombe sister species, S. japonicus has provided critical insights into the workings and the evolution of cell biological mechanisms. Furthermore, its divergent biology makes S. japonicus a valuable model organism in its own right. However, the currently available short-read genome assembly contains gaps and has been unable to resolve centromeres and other repeat-rich chromosomal regions. Here we present a telomere-to-telomere long-read genome assembly of the S japonicus genome. This includes the three megabase-length chromosomes, with centromeres hundreds of kilobases long, rich in 5S ribosomal RNAs, transfer RNAs, long terminal repeats, and short repeats. We identify a gene-sparse region on chromosome 2 that resembles a 331 kb centromeric duplication. We revise the genome size of S. japonicus to at least 16.6 Mb and possibly up to 18.12 Mb, at least 30% larger than previous estimates. Our whole genome assembly will support the growing S. japonicus research community and facilitate research in new directions, including centromere and DNA repeat evolution, and yeast comparative genomics. Take-awayO_LIA telomere-to-telomere genome assembly of the fission yeast S. japonicus C_LIO_LIChromosome 2 harbours a previously unknown second centromere-like region C_LIO_LIThe estimated genome size of S. japonicus may be up to 18.12 Mb C_LI

genomics↗

bioRxiv2023

Thermodynamic Forces from Protein and Water Govern Condensate Formation of an Intrinsically Disordered Protein Domain

Liquid-liquid phase separation (LLPS) can drive a multitude of cellular processes by compartmentalizing biological cells via the formation of dense liquid biomolecular condensates, which can function as membraneless organelles. Despite its importance, the molecular-level understanding of the underlying thermodynamics of this process remains incomplete. In this study, we use atomistic molecular dynamics simulations of the low complexity domain (LCD) of human fused in sarcoma (FUS) protein to investigate the contributions of water and protein molecules to the free energy changes that govern LLPS. Both protein and water components are found to have comparably sizeable thermodynamic contributions to the formation of FUS condensates. Moreover, we quantify the counteracting effects of water molecules that are released into the bulk upon condensate formation and the waters retained within the protein droplets. Among the various factors considered, solvation entropy and protein interaction enthalpy are identified as the most important contributions, while solvation enthalpy and protein entropy changes are smaller. These insights provide detailed molecular insights on the intricate thermodynamic interplay between protein- and solvation-related forces underlying the formation of biomolecular condensates.

biophysics↗

bioRxiv2023

Conserved nucleocytoplasmic density homeostasis drives cellular organization across eukaryotes

The packing and confinement of macromolecules in the cytoplasm and nucleoplasm has profound implications for cellular biochemistry. How intracellular density distributions vary and affect cellular physiology remains largely unknown. Here, we show that the nucleus is less dense than the cytoplasm and that living systems establish and maintain a constant density ratio between these compartments. Using label-free biophotonics and theory, we show that nuclear density is set by a pressure balance across the nuclear envelope in vitro, in vivo and during early development. Nuclear transport establishes a specific nuclear proteome that exerts a colloid osmotic pressure, which, assisted by entropic chromatin pressure, draws water into the nucleus. Using C. elegans, we show that while nuclear-to-cytoplasmic (N/C) volume ratios change during early development, the N/C density ratio is robustly maintained. We propose that the maintenance of a constant N/C density ratio is the biophysical driver of one of the oldest tenets of cell biology: the N/C volume ratio. In summary, this study reveals a previously unidentified homeostatic coupling of macromolecular densities that drives cellular organization with implications for pathophysiologies such as senescence and cancer.

biophysics↗

bioRxiv2023

EpiSegMix: A Flexible Distribution Hidden Markov Model with Duration Modeling for Chromatin State Discovery

MotivationAutomated chromatin segmentation based on ChIP-seq data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. ResultsWe introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with two existing tools, ChromHMM, Segway and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. Availability and implementationSource code: https://gitlab.com/rahmannlab/episegmix.

bioinformatics↗

bioRxiv2023

Cold Storage and Cryopreservation Methods for Spermatozoa of the Sea Urchin, Lytechinus pictus .

Sea urchins have contributed greatly to knowledge of fertilization, embryogenesis and cell biology. However, until now, they have not been a genetic model organism because of the long generation times of commonly used species, and lack of tools for husbandry and genetic manipulation. We recently established Lytechinus pictus, as a multigenerational sea urchin model, because of its relatively short generation time of 4-6 months and ease of laboratory culture. To take full advantage of this new multigenerational species, methods are needed to biobank and share mutant L. pictus sperm. Here, we describe a new extender based on sperm ion physiology before spawning of sperm into seawater. This extender maintains sperm capable of fertilization for at least 5-10 weeks when stored at 0 {degrees}C. We use the extender, and the cryoprotectant dimethyl sulfoxide (DMSO), to cryopreserve sperm of both L. pictus, and the widely used sea urchin, Strongylocentrotus purpuratus. The simple methods we describe work well for both species, achieving > 90% development and producing larvae that successfully undergo metamorphosis to juvenile sea urchins. Sperm of these two species can be frozen and thawed at least twice and still give rise to larvae that undergo metamorphosis. Main PointsO_LISperm can maintain fertilizing capacity ex vivo for 5-10 weeks when stored at 0{degrees}C. C_LIO_LIWhen freezing in liquid nitrogen no stepwise addition of cryoprotectant, or stepwise drop in temperature are required. C_LIO_LIA standard fertilization assay is presented to score cleavage stage sea urchin embryos produced by cryopreserved sperm. C_LIO_LISperm frozen and thawed more than once can produce larvae. C_LI

developmental biology↗

bioRxiv2023

Elevated temperature fatally disrupts nuclear divisions in the early Drosophila embryo.

Temperature variations can challenge animal survival, with elevated temperatures presenting distinct vulnerabilities at different stages of the animal life cycle. Embryonic development is known to be especially vulnerable, though the molecular mechanisms that underlie this vulnerability remain unclear. Here we investigate how elevated temperature affects embryogenesis in Drosophila melanogaster, an insect model known for extensively characterized genetic makeup and developmental processes. We identify the first three hours as a critical window in which Drosophila embryos are particularly vulnerable to elevated temperature. This period includes the formation of a syncytial blastoderm, which involves 4 rounds of meta-synchronous nuclear divisions at the embryonic cortex, and cellularization that leads to the formation of the cellular blastoderm. Embryos exposed to elevated temperature during this period subsequently exhibited developmental defects at the gastrulation stage, leading to increased lethality. At elevated temperature, we observed an increase in mitotic failures causing a loss of cortical nuclei during cellularization. There is also a local crowding of nuclei and an increase in asynchrony between the nuclear cycles in the center vs. the poles. Interestingly, these features cooperatively amplify the frequency of mitotic failures, leading to holes in the blastoderm epithelium. Further mechanistic analysis of mitotic failures revealed that they trigger DNA damage response. Focusing on cell biological causes of mitotic failures, we found that the interaction between F-actin and microtubules is weakened at elevated temperature. We performed functional tests to determine whether known regulators of mitosis could rescue mitotic failures, blastoderm hole formation, and improve embryo survival. We further analyzed genomic datasets from wild populations to determine whether these regulators also carry signatures of adaptation in wild populations. Our genetic rescue experiments show that in Drosophila embryos, the interaction between cortical F-actin and astral microtubules of mitotic spindle is vulnerable to disruption at elevated temperatures, leading to mitotic failures that can be potentially rescued by modulating the expression of just a few factors. We propose that levels of expression of corresponding genes could be used as indicators to predict the effects of increasing temperature variations on insect populations.

developmental biology↗

bioRxiv2023

Detecting Differential Alternative Splicing in Mass Spectrometry-based Proteomics Data

Alternative splicing can substantially diversify biological cell states and influence cellular function. The functional impact of splicing has to be estimated at protein level, typically by mass spectrometry (MS) -based proteomics. Although this technology measures increasingly large peptides sets, distinguishing isoform-specific peptides are rare, limiting detection and quantification of splicing. We introduce MS-EmpiReS, a quantification-based computational approach for differential alternative splicing detection in proteomics data. Its core principle is to differentially quantify peptides mapping to different regions of genes. This approach increased the number of testable peptides hundred-fold in a clinical cancer cohort, resulting in a large number of cancer-relevant splicing candidates. Splicing events detected by both MS-EmpiReS and deep RNA sequencing correlated well but also provided complementary information. The proteomics data allowed us to define a per-sample splicing score to separate cancer conditions. Finally, deep brain proteomes from different mice separated strongly by the lower abundance protein splicing isoform.

bioinformatics↗

bioRxiv2023

Full-Length Single-Molecule Protein Fingerprinting

Proteins are the primary functional actors of the cell. Hence, their identification is pivotal to advance our understanding of cell biology and disease. Current protein analysis methods are of limited use for distinguishing proteoforms. In particular, mass spectrometric methods often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate FRET-based single-molecule protein fingerprinting to map the location of individual amino acids and a post-translational modification within single full-length protein molecules. Using an approach that relies on transient binding of fluorescently labeled DNA strands to probe the amino acids on a protein one by one we show that we can fingerprint intrinsically disordered proteins as well as folded globular proteins with sub-nanometer resolution. We anticipate that this technology will be used for proteoform identification in biological and translational research with ultimate sensitivity.

biophysics↗

bioRxiv2023

Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth

AO_SCPLOWBSTRACTC_SCPLOWBacterial chromosomes are spatiotemporally organized and sensitive to environmental changes. However, the mechanisms underlying chromosome configuration and reorganization are currently not fully understood. Using 3D single-molecule localization microscopy and live-cell imaging, we show that the Escherichia coli nucleoid adopts a condensed, membrane-associated configuration during rapid growth. To study the influence of different biosynthetic processes on nucleoid morphology and positioning, we recorded multi-colour super-resolution images during drug treatment. After developing analysis routines for confocal and super-resolution images, we captured highly resolved snapshots which revealed the complete loss of the membrane-bound state of the nucleoid within 10 minutes of halting transcription and translation. This indicates an active role of transertion (coupled transcription, translation and membrane insertion) in nucleoid organization. In contrast, cell wall synthesis inhibition only affects nucleoid organization during morphological changes. Further, we provide evidence that the E. coli nucleoid spatially correlates with MreB in unperturbed E. coli cells, while this correlation diminishes in cells with changed cell geometry or upon inhibition of protein biosynthesis. Replication inhibition experiments, as well as multi-drug treatments highlight the role of entropic effects and transcription in nucleoid condensation and positioning. In summary, we provide experimental evidence for transertion as a principal organiser of the bacterial nucleoid, and show that an altered metabolic state and antibiotic treatment both lead to major changes in the degree of transertion and overall spatial organization of the nucleoid. Our high-resolution characterization reveals dynamics of antibiotic action and provides tools to quantify bacterial chromosome organization. This does not only provide valuable insights into the role of transertion, but can also be applied to study other cell-biological processes.

microbiology↗

bioRxiv2023

PyTME (Python Template Matching Engine): A fast, flexible, and multi-purpose template matching library for cryogenic electron microscopy data

Cryogenic electron microscopy (cryo-EM) is a key method in structural and cell biology. Analysis of cryo-EM images requires interpretation of noisy, low-resolution densities which relies on identifying the most probable orientation of macromolecules in a target using template matching. Many method-specific template matching software exist for single-particle cryo-EM, cryo-electron tomography (cryo-ET), or fitting atomic structures into averaged 3D maps of macromolecules. Here, we report the Python Template Matching Engine (pyTME), a software engine that consolidates method-specific template matching problems. The underlying library provides highly efficient template-matching implementation and abstract data structures for storing and manipulating input and output data. It scales favorable to large datasets, both with multiple CPUs and GPUs, compared to existing software enabling template matching of even unbinned cryo-ET data in hours, which was previously nearly impossible due to technical restraints. Any hardware-specific optimization needed for dealing with large data is automatically performed to increase ease of use and minimize user intervention. The efficiency and simplicity of pyTME will enable high throughput mining of a variety of cryo-EM and ET datasets in the future.

bioinformatics↗

bioRxiv2023

IL-27 maintains cytotoxic Ly6C+ gamma delta T cells that arise from immature precursors

In mice, {gamma}{delta} T cells that express the co-stimulatory molecule, CD27, are committed to the IFN{gamma}-producing lineage in the thymus, and in the periphery, these cells play a critical role in host defence and anti-tumor immunity. Unlike {beta} T cells that rely on MHC-presented peptides to drive their terminal differentiation, it is unclear whether MHC-unrestricted {gamma}{delta} T cells undergo further functional maturation after exiting the thymus. Here, we provide evidence of phenotypic and functional diversity within peripheral IFN{gamma}-producing {gamma}{delta} T cells. We found that immature CD27+Ly6C-- cells convert into mature CD27+Ly6C+ cells, and these mature cells control cancer progression while the immature cells cannot. The gene signatures of these two subsets were highly analogous to human immature and mature {gamma}{delta} T cells, indicative of conservation across species. We show that IL-27 supports the cytotoxic phenotype and function of mouse CD27+Ly6C+ cells and human V{delta}2+ cells, while IL-27 is dispensable for mouse CD27+Ly6C-- cells and human V{delta}1+ cells. These data reveal increased complexity within IFN{gamma}-producing {gamma}{delta} T cells, comprising of immature and terminally differentiated subsets, that offer new insights into unconventional T cell biology.

immunology↗

bioRxiv2023

HiTIPS: High-Throughput Image Processing Software for the Study of Nuclear Architecture and Gene Expression

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software for image analysis workflows does not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new algorithms for existing analysis pipelines and to adding new analysis pipelines through separate plugins. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI analysis platform for a variety of cell biology applications.

bioinformatics↗

bioRxiv2023

A non-adaptive explanation for macroevolutionary patterns in the evolution of complex multicellularity

"Complex multicellularity", conventionally defined as large organisms with many specialized cell types, has evolved five times independently in eukaryotes, but never within prokaryotes. A number hypotheses have been proposed to explain this phenomenon, most of which posit that eukaryotes evolved key traits (e.g., dynamic cytoskeletons, alternative mechanisms of gene regulation, or subcellular compartments) which were a necessary prerequisite for the evolution of complex multicellularity. Here we propose an alternative, non-adaptive hypothesis for this broad macroevolutionary pattern. By binning cells into groups with finite genetic bottlenecks between generations, the evolution of multicellularity greatly reduces the effective population size (Ne) of cellular populations, increasing the role of genetic drift in evolutionary change. While both prokaryotes and eukaryotes experience this phenomenon, they have opposite responses to drift: mutational biases in eukaryotes tend to drive genomic expansion, providing additional raw genetic material for subsequent multicellular innovation, while prokaryotes generally face genomic erosion. These effects become more severe as organisms evolve larger size and more stringent genetic bottlenecks between generations-- both of which are hallmarks of complex multicellularity. Taken together, we hypothesize that it is these idiosyncratic lineagespecific mutational biases, rather than cell-biological innovations within eukaryotes, that underpins the long-term divergent evolution of complex multicellularity across the tree of life.

evolutionary biology↗